class_docstr: A simple segmentation accuracy metric
  
  Calculates segmentation accuracy.
  
  # Arguments
      ignore_class: class to ignore during metric computation (e.g. void class).
code: def accumulate(self, preds):
      if self._seg_masks_pred is None:
          self._seg_masks_pred = np.stack(
              [x.pred.segmentation.mask_array.data for x in preds]
          )
      else:
          self._seg_masks_pred = np.concatenate(
              [
                  self._seg_masks_pred,
                  np.stack(
                      [
                          x.pred.segmentation.mask_array.data
                          for x in preds
                      ]
                  ),
              ]
          )
  
      if self._seg_masks_gt is None:
          self._seg_masks_gt = np.stack(
              [
                  x.ground_truth.segmentation.mask_array.data
                  for x in preds
              ]
          )
      else:
          self._seg_masks_gt = np.concatenate(
              [
                  self._seg_masks_gt,
                  np.stack(
                      [
                          x.ground_truth.segmentation.mask_array.data
                          for x in preds
                      ]
                  ),
              ],
          )
  
file_path: icevision/icevision/metrics/segmentation_accuracy/segmentation_accuracy.py
imports: from icevision.imports import *
  from icevision.utils import *
  from icevision.data import *
  from icevision.metrics.metric import *
label_desc: Use image node_id 3 for calling the SegmentationAccuracy obj's underlying member method code with example usage: obj.accumulate(preds) without return types